Apparatus and method for processing video data

ABSTRACT

An apparatus and methods for processing video data are described. The invention provides a representation of video data that can be used to assess agreement between the data and a fitting model for a particular parameterization of the data. This allows the comparison of different parameterization techniques and the selection of the optimum one for continued video processing of the particular data. The representation can be utilized in intermediate form as part of a larger process or as a feedback mechanism for processing video data. When utilized in its intermediate form, the invention can be used in processes for storage, enhancement, refinement, feature extraction, compression, coding, and transmission of video data. The invention serves to extract salient information in a robust and efficient manner while addressing the problems typically associated with video data sources.

This application claims the priority of U.S. Provisional Application No.60/611,878, titled “System And Method For Video Compression EmployingPrincipal Component Analysis,” filed Sep. 21, 2004. This application isa continuation-in-part of U.S. application Ser. No. 11/191,562, filedJul. 28, 2005. Each of the foregoing applications is incorporated hereinby reference in its entirety.

FIELD OF THE INVENTION

The present invention is generally related to the field of digitalsignal processing, and more particularly, to computer apparatus andcomputer-implemented methods for the efficient representation andprocessing of signal or image data, and most particularly, video data.

DESCRIPTION OF THE PRIOR ART

The general system description of the prior art in which the currentinvention resides can be expressed as in FIG. 1. Here a block diagramdisplays the typical prior art video processing system. Such systemstypically include the following stages: an input stage 102, a processingstage 104, an output stage 106, and one or more data storagemechanism(s) 108.

The input stage 102 may include elements such as camera sensors, camerasensor arrays, range finding sensors, or a means of retrieving data froma storage mechanism. The input stage provides video data representingtime correlated sequences of man-made and/or naturally occurringphenomena. The salient component of the data may be masked orcontaminated by noise or other unwanted signals.

The video data, in the form of a data stream, array, or packet, may bepresented to the processing stage 104 directly or through anintermediate storage element 108 in accordance with a predefinedtransfer protocol. The processing stage 104 may take the form ofdedicated analog or digital devices, or programmable devices such ascentral processing units (CPUs), digital signal processors (DSPs), orfield programmable gate arrays (FPGAs) to execute a desired set of videodata processing operations. The processing stage 104 typically includesone or more CODECs (COder/DECcoders).

Output stage 106 produces a signal, display, or other response which iscapable of affecting a user or external apparatus. Typically, an outputdevice is employed to generate an indicator signal, a display, ahardcopy, a representation of processed data in storage, or to initiatetransmission of data to a remote site. It may also be employed toprovide an intermediate signal or control parameter for use insubsequent processing operations.

Storage is presented as an optional element in this system. Whenemployed, storage element 108 may be either non-volatile, such asread-only storage media, or volatile, such as dynamic random accessmemory (RAM). It is not uncommon for a single video processing system toinclude several types of storage elements, with the elements havingvarious relationships to the input, processing, and output stages.Examples of such storage elements include input buffers, output buffers,and processing caches.

The primary objective of the video processing system in FIG. 1 is toprocess input data to produce an output which is meaningful for aspecific application. In order to accomplish this goal, a variety ofprocessing operations may be utilized, including noise reduction orcancellation, feature extraction, object segmentation and/ornormalization, data categorization, event detection, editing, dataselection, data re-coding, and transcoding.

Many data sources that produce poorly constrained data are of importanceto people, especially sound and visual images. In most cases theessential characteristics of these source signals adversely impact thegoal of efficient data processing. The intrinsic variability of thesource data is an obstacle to processing the data in a reliable andefficient manner without introducing errors arising from naive empiricaland heuristic methods used in deriving engineering assumptions. Thisvariability is lessened for applications when the input data arenaturally or deliberately constrained into narrowly definedcharacteristic sets (such as a limited set of symbol values or a narrowbandwidth). These constraints all too often result in processingtechniques that are of low commercial value.

The design of a signal processing system is influenced by the intendeduse of the system and the expected characteristics of the source signalused as an input. In most cases, the performance efficiency requiredwill also be a significant design factor. Performance efficiency, inturn, is affected by the amount of data to be processed compared withthe data storage available as well as the computational complexity ofthe application compared with the computing power available.

Conventional video processing methods suffer from a number ofinefficiencies which are manifested in the form of slow datacommunication speeds, large storage requirements, and disturbingperceptual artifacts. These can be serious problems because of thevariety of ways people desire to use and manipulate video data andbecause of the innate sensitivity people have for some forms of visualinformation.

An “optimal” video processing system is efficient, reliable, and robustin performing a desired set of processing operations. Such operationsmay include the storage, transmission, display, compression, editing,encryption, enhancement, categorization, feature detection, andrecognition of the data. Secondary operations may include integration ofsuch processed data with other information sources. Equally important,in the case of a video processing system, the outputs should becompatible with human vision by avoiding the introduction of perceptualartifacts.

A video processing system may be described as “robust” if its speed,efficiency, and quality do not depend strongly on the specifics of anyparticular characteristics of the input data. Robustness also is relatedto the ability to perform operations when some of the input iserroneous. Many video processing systems fail to be robust enough toallow for general classes of applications—providing only application tothe same narrowly constrained data that was used in the development ofthe system.

Salient information can be lost in the discretization of acontinuous-valued data source due to the sampling rate of the inputelement not matching the signal characteristics of the sensed phenomena.Also, there is loss when the signal's strength exceeds the sensor'slimits, resulting in saturation. Similarly, information is lost when theprecision of input data is reduced as happens in any quantizationprocess when the full range of values in the input data is representedby a set of discrete values, thereby reducing the precision of the datarepresentation.

Ensemble variability refers to any unpredictability in a class of dataor information sources. Data representative of visual information has avery large degree of ensemble variability because visual information istypically unconstrained. Visual data may represent any spatial arraysequence or spatio-temporal sequence that can be formed by lightincident on a sensor array.

In modeling visual phenomena, video processors generally impose some setof constraints and/or structure on the manner in which the data isrepresented or interpreted. As a result, such methods can introducesystematic errors which would impact the quality of the output, theconfidence with which the output may be regarded, and the type ofsubsequent processing tasks that can reliably be performed on the data.

Quantization methods reduce the precision of data in the video frameswhile attempting to retain the statistical variation of that data.Typically, the video data is analyzed such that the distributions ofdata values are collected into probability distributions. There are alsomethods that project the data into phase space in order to characterizethe data as a mixture of spatial frequencies, thereby allowing precisionreduction to be diffused in a less objectionable manner. When utilizedheavily, these quantization methods often result in perceptuallyimplausible colors and can induce abrupt pixilation in originally smoothareas of the video frame.

Differential coding is also typically used to capitalize on the localspatial similarity of data. Data in one part of the frame tend to beclustered around similar data in that frame, and also in a similarposition in subsequent frames. Representing the data in terms of it'sspatially adjacent data can then be combined with quantization and thenet result is that, for a given precision, representing the differencesis more accurate than using the absolute values of the data. Thisassumption works well when the spectral resolution of the original videodata is limited, such as in black and white video, or low-color video.As the spectral resolution of the video increases, the assumption ofsimilarity breaks down significantly. The breakdown is due to theinability to selectively preserve the precision of the video data.

Residual coding is similar to differential encoding in that the error ofthe representation is further differentially encoded in order to restorethe precision of the original data to a desired level of accuracy.

Variations of these methods attempt to transform the video data intoalternate representations that expose data correlations in spatial phaseand scale. Once the video data has been transformed in these ways,quantization and differential coding methods can then be applied to thetransformed data resulting in an increase in the preservation of thesalient image features. Two of the most prevalent of these transformvideo compression techniques are the discrete cosine transform (DCT) anddiscrete wavelet transform (DWT). Error in the DCT transform manifestsin a wide variation of video data values, and therefore, the DCT istypically used on blocks of video data in order to localize these falsecorrelations. The artifacts from this localization often appear alongthe border of the blocks. For the DWT, more complex artifacts happenwhen there is a mismatch between the basis function and certaintextures, and this causes a blurring effect. To counteract the negativeeffects of DCT and DWT, the precision of the representation is increasedto lower distortion at the cost of precious bandwidth.

SUMMARY OF THE INVENTION

The present invention is a computer-implemented video processing methodthat provides both computational and analytical advantages over existingstate-of-the-art methods. The principle inventive method is theintegration of a linear decompositional method, a spatial segmentationmethod, and a spatial normalization method. Spatially constraining videodata greatly increases the robustness and applicability of lineardecompositional methods. Additionally, spatial segmentation of the datacan mitigate induced nonlinearity when other high variance data isspatially adjacent to the data being analyzed.

In particular, the present invention provides a means by which signaldata can be efficiently processed into one or more beneficialrepresentations. The present invention is efficient at processing manycommonly occurring data sets and is particularly efficient at processingvideo and image data. The inventive method analyzes the data andprovides one or more concise representations of that data to facilitateits processing and encoding. Each new, more concise data representationallows reduction in computational processing, transmission bandwidth,and storage requirements for many applications, including, but notlimited to: coding, compression, transmission, analysis, storage, anddisplay of the video data. The invention includes methods foridentification and extraction of salient components of the video data,allowing a prioritization in the processing and representation of thedata. Noise and other unwanted parts of the signal are identified aslower priority so that further processing can be focused on analyzingand representing the higher priority parts of the video signal. As aresult, the video signal is represented more concisely than waspreviously possible. And the loss in accuracy is concentrated in theparts of the video signal that are perceptually unimportant.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a prior art video processingsystem.

FIG. 2 is a block diagram providing an overview of the invention thatshows the major modules for processing video.

FIG. 3 is a block diagram illustrating the motion estimation method ofthe invention.

FIG. 4 is a block diagram illustrating the global registration method ofthe invention.

FIG. 5 is a block diagram illustrating the normalization method of theinvention.

FIG. 6 is a block diagram illustrating the hybrid spatial normalizationcompression method.

DETAILED DESCRIPTION

In video signal data, frames of video are assembled into a sequence ofimages usually depicting a three dimensional scene as projected onto atwo dimensional imaging surface. Each frame, or image, is composed ofpicture elements (pels) that represent an imaging sensor response to thesampled signal. Often, the sampled signal corresponds to some reflected,refracted, or emitted electromagnetic energy sampled by a twodimensional sensor array. A successive sequential sampling results in aspatiotemporal data stream with two spatial dimensions per frame and atemporal dimension corresponding to the frame's order in the videosequence.

The present invention as illustrated in FIG. 2 analyzes signal data andidentifies the salient components. When the signal is comprised of videodata, analysis of the spatiotemporal stream reveals salient componentsthat are often specific objects, such as faces. The identificationprocess qualifies the existence and significance of the salientcomponents, and chooses one or more of the most significant of thosequalified salient components. This does not limit the identification andprocessing of other less salient components after or concurrently withthe presently described processing. The aforementioned salientcomponents are then further analyzed, identifying the variant andinvariant subcomponents. The identification of invariant subcomponentsis the process of modeling some aspect of the component, therebyrevealing a parameterization of the model that allows the component tobe synthesized to a desired level of accuracy.

In one embodiment of the invention, a foreground object is detected andtracked. The object's pels are identified and segmented from each frameof the video. The block-based motion estimation is applied to thesegmented object in multiple frames. These motion estimates are thenintegrated into a higher order motion model. The motion model isemployed to warp instances of the object to a common spatialconfiguration. For certain data, in this configuration, more of thefeatures of the object are aligned. This normalization allows the lineardecomposition of the values of the object's pels over multiple frames tobe compactly represented. The salient information pertaining to theappearance of the object is contained in this compact representation.

A preferred embodiment of the present invention details the lineardecomposition of a foreground video object. The object is normalizedspatially, thereby yielding a compact linear appearance model. A furtherpreferred embodiment additionally segments the foreground object fromthe background of the video frame prior to spatial normalization.

A preferred embodiment of the invention applies the present invention toa video of a person speaking into a camera while undergoing a smallamount of motion.

A preferred embodiment of the invention applies the present invention toany object in a video that can be represented well through spatialtransformations.

A preferred embodiment of the invention specifically employs block-basedmotion estimation to determine finite differences between two or moreframes of video. A higher order motion model is factored from the finitedifferences in order to provide a more effective linear decomposition.

Detection & Tracking

Once the constituent salient components of the signal have beendetermined, these components may be retained, and all other signalcomponents may be diminished or removed. The process of detecting thesalient component is shown in FIG. 2, where the Video Frame (202) isprocessed by one or more Detect Object (206) processes, resulting in oneor more objects being identified, and subsequently tracked. The retainedcomponents represent the intermediate form of the video data. Thisintermediate data can then be encoded using techniques that aretypically not available to existing video processing methods. As theintermediate data exists in several forms, standard video encodingtechniques can also be used to encode several of these intermediateforms. For each instance, the present invention determines and thenemploys the encoding technique that is most efficient.

In one preferred embodiment, a saliency analysis process detects andclassifies salient signal modes. One embodiment of this process employsa combination of spatial filters specifically designed to generate aresponse signal whose strength is relative to the detected saliency ofan object in the video frame. The classifier is applied at differingspatial scales and in different positions of the video frame. Thestrength of the response from the classifier indicates the likelihood ofthe presence of a salient signal mode. When centered over a stronglysalient object, the process classifies it with a correspondingly strongresponse. The detection of the salient signal mode distinguishes thepresent invention by enabling the subsequent processing and analysis onthe salient information in the video sequence.

Given the detection location of a salient signal mode in one or moreframes of video, the present invention analyzes the salient signalmode's invariant features. Additionally, the invention analyzes theresidual of the signal, the “less-salient” signal modes, for invariantfeatures. Identification of invariant features provides a basis forreducing redundant information and segmenting (i.e. separating) signalmodes.

Feature Point Tracking

In one embodiment of the present invention, spatial positions in one ormore frames are determined through spatial intensity field gradientanalysis. These features correspond to some intersection of “lines”which can be described loosely as a “corner”. Such an embodiment furtherselects a set of such corners that are both strong corners and spatiallydisparate from each other, herein referred to as the feature points.Further, employing a hierarchical multi-resolution estimation of theoptical flow allows the determination of the translational displacementof the feature points over time.

In FIG. 2, the Track Object (220) process is shown to pull together thedetection instances from the Detect Object processes (208) and furtherIdentify Correspondences (222) of features of one or more of thedetected objects over a multitude of Video Frames (202 & 204).

A non-limiting embodiment of feature tracking can be employed such thatthe features are used to qualify a more regular gradient analysis methodsuch as block-based motion estimation.

Another embodiment anticipates the prediction of motion estimates basedon feature tracking.

Object-Based Detection and Tracking

In one non-limiting embodiment of the current invention, a robust objectclassifier is employed to track faces in frames of video. Such aclassifier is based on a cascaded response to oriented edges that hasbeen trained on faces. In this classifier, the edges are defined as aset of basic Haar features and the rotation of those features by 45degrees. The cascaded classifier is a variant of the AdaBoost algorithm.Additionally, response calculations can be optimized through the use ofsummed area tables.

Local Registration

Registration involves the assignment of correspondences between elementsof identified objects in two or more video frames. These correspondencesbecome the basis for modeling the spatial relationships between videodata at temporally distinct points in the video data.

Various non-limiting means of registration are described for the presentinvention in order to illustrate specific embodiments and theirassociated reductions to practice in terms of well known algorithms andinventive derivatives of those algorithms.

One means of modeling the apparent optical flow in a spatio-temporalsequence can be achieved through generation of a finite difference fieldfrom two or more frames of the video data. Optical flow field can besparsely estimated if the correspondences conform to certain constancyconstraints in both a spatial and an intensity sense.

As shown in FIG. 3, a Frame (302 or 304) is sub-sampled spatially,possibly through a decimation process (306), or some other sub-samplingprocess (e.g. low pass filter). These spatially reduced images (310 &312) can be further sub-sampled as well.

Diamond Search

Given a non-overlapping partitioning of a frame of video into blocks,search the previous frame of video for a match to each block. The fullsearch block-based (FSBB) motion estimation finds the position in theprevious frame of video that has the lowest error when compared with ablock in the current frame. Performing FSBB can be quite expensivecomputationally, and often does not yield a better match than othermotion estimation schemes based on the assumption of localized motion.Diamond search block-based (DSBB) gradient descent motion estimation isa common alternative to FSBB that uses a diamond shaped search patternof various sizes to iteratively traverse an error gradient toward thebest match for a block.

In one embodiment of the present invention, DSBB is employed in theanalysis of the image gradient field between one or more frames of videoin order to generate finite differences whose values are later factoredinto higher order motion models.

One skilled in the art is aware that block-based motion estimation canbe seen as the equivalent of an analysis of vertices of a regular mesh.

Phase-Based Motion Estimation

In the prior art, block-based motion estimation typically implemented asa spatial search resulting in one or more spatial matches. Phase-basednormalized cross correlation (PNCC) as illustrated in FIG. 3 transformsa block from the current frame and the previous frame into “phase space”and finds the cross correlation of those two blocks. The crosscorrelation is represented as a field of values whose positionscorrespond to the ‘phase shifts’ of edges between the two blocks. Thesepositions are isolated through thresholding and then transformed backinto spatial coordinates. The spatial coordinates are distinct edgedisplacements, and correspond to motion vectors.

Advantages of the PNCC include contrast masking which allows thetolerance of gain/exposure adjustment in the video stream. Also, thePNCC allows results from a single step that might take many iterationsfrom a spatially based motion estimator. Further, the motion estimatesare sub-pixel accurate.

One embodiment of the invention utilizes PNCC in the analysis of theimage gradient field between one or more frames of video in order togenerate finite differences whose values are later factored into higherorder motion models.

Global Registration

In one embodiment, the present invention factors one or more linearmodels from a field of finite difference estimations. The field fromwhich such sampling occurs is referred to herein as the generalpopulation of finite differences. The described method employs robustestimation similar to that of the RANSAC algorithm.

As shown in FIG. 4, the finite differences, in the case of global motionmodeling, are Translational Motion Estimates (402) that are collectedinto a General Population Pool (404) which is iteratively processed by aRandom Sampling of those Motion Estimates (410) and a linear model isfactored out (420) of those samples. The Results are then used to adjustthe population (404) to better clarify the linear model through theexclusion of outliers to the model, as found through the random process.

In one embodiment of the linear model estimation algorithm, the motionmodel estimator is based on a linear least squares solution. Thisdependency causes the estimator to be thrown off by outlier data. Basedon RANSAC, the disclosed method is a robust method of countering theeffect of outliers through the iterative estimation of subsets of thedata, probing for a motion model that will describe a significant subsetof the data. The model generated by each probe is tested for thepercentage of the data that it represents. If there are a sufficientnumber of iterations, then a model will be found that fits the largestsubset of the data.

As conceived and illustrated in FIG. 4, the present invention disclosesinnovations beyond the RANSAC algorithm in the form of alterations ofthe algorithm that involve the initial sampling of finite differences(samples) and least squares estimation of a linear model. Synthesiserror is assessed for all samples in the general population using thesolved linear model. A rank is assigned to the linear model based on thenumber of samples whose residual conforms to a preset threshold. Thisrank is considered the “candidate consensus”.

The initial sampling, solving, and ranking are performed iterativelyuntil termination criteria are satisfied. Once the criteria aresatisfied, the linear model with the greatest rank is considered to bethe final consensus of the population.

An option refinement step involves iteratively analyzing subsets ofsamples in the order of best fit to the candidate model, and increasingthe subset size until adding one more sample would exceed a residualerror threshold for the whole subset.

As shown in FIG. 4, The Global Model Estimation process (450) isiterated until the Consensus Rank Acceptability test is satisfied (452).When the rank has not been achieved, the population of finitedifferences (404) is sorted relative to the discovered model in aneffort to reveal the linear model. The best (highest rank) motion modelis added to a solution set in process 460. Then the model isre-estimated in process 470. Upon completion, the population (404) isre-sorted.

The described non-limiting embodiments of the invention can be furthergeneralized as a general method of sampling a vector space, describedabove as a field of finite difference vectors, in order to determinesubspace manifolds in another parameter vector space that wouldcorrespond to a particular linear model.

A further result of the global registration process is that thedifference between this and the local registration process yields alocal registration residual. This residual is the error of the globalmodel in approximating the local model.

Normalization

Normalization refers to the resampling of spatial intensity fieldstowards a standard, or common, spatial configuration. When theserelative spatial configurations are invertible spatial transformationsbetween such configurations the resampling and accompanyinginterpolation of pels are also invertible up to a topological limit. Thenormalization method of the present invention is illustrated in FIG. 5.

When more than two spatial intensity fields are normalized, increasedcomputational efficiency may be achieved by preserving intermediatenormalization calculations.

Spatial transformation models used to resample images for the purpose ofregistration, or equivalently for normalization, include global andlocal models. Global models are of increasing order from translationalto projective. Local models are finite differences that imply aninterpolant on a neighborhood of pels as determined basically by a blockor more complexly by a piece-wise linear mesh.

Interpolation of original intensity fields to normalized intensity fieldincreases linearity of PCA appearance models based on subsets of theintensity field.

As shown in FIG. 2, the object pels (232 & 234) can be re-sampled (240)to yield a normalized version of the object pels (242 & 244).

Three Dimensional Normalization

A further embodiment of the present invention tessellates the featurepoints into a triangle based mesh, the vertices of the mesh are tracked,and the relative positions of each triangle's vertices are used toestimate the three-dimensional surface normal for the plane coincidentwith those three vertices. When the surface normal is coincident withthe projective axis of the camera, the imaged pels can provide aleast-distorted rendering of the object corresponding to the triangle.Creating a normalized image that tends to favor the orthogonal surfacenormal can produce a pel preserving intermediate data type that willincrease the linearity of subsequent appearance-based PCA models.

Another embodiment utilizes conventional block-based motion estimationto implicitly model a global motion model. In one, non-limitingembodiment, the method factors a global affine motion model from themotion vectors described by the conventional block-based motionestimation/prediction.

Progressive Geometric Normalization

Classification of spatial discontinuities is used to align tessellatedmesh in order to model discontinuities implicitly as they are coincidentwith mesh edges.

Homogeneous region boundaries are approximated by a polygon contour. Thecontour is successively approximated in order to determine the priorityof each original vertex. Vertex priority is propagated across regions inorder to preserve vertex priority for shared vertices.

Image registration is biased towards high priority vertices with strongimage gradients. Resulting deformation models tend to preserve spatialdiscontinuities associated with the geometry of the imaged object.

Segmentation

The spatial discontinuities identified through the further describedsegmentation processes are encoded efficiently through geometricparameterization of their respective boundaries, referred to as spatialdiscontinuity models. These spatial discontinuity models may be encodedin a progressive manner allowing for ever more concise boundarydescriptions corresponding to subsets of the encoding. Progressiveencoding provides a robust means of prioritizing the spatial geometrywhile retaining much of the salient aspects of the spatialdiscontinuities.

A preferred embodiment of the present invention combines amulti-resolution segmentation analysis with the gradient analysis of thespatial intensity field and further employs a temporal stabilityconstraint in order to achieve a robust segmentation.

As shown in FIG. 2, once the correspondences of feature of an objecthave been tracked over time (220) and modeled (224), adherence to thismotion/deformation model can be used to segment the pels correspondingto the object (230). This process can be repeated for a multitude ofdetected objects (206 & 208) in the video (202 & 204). The results ofthis processing are the segmented object pels (232).

One form of invariant feature analysis employed by the present inventionis focused on the identification of spatial discontinuities. Thesediscontinuities manifest as edges, shadows, occlusions, lines, corners,or any other visible characteristic that causes an abrupt andidentifiable separation between pels in one or more imaged frames ofvideo. Additionally, subtle spatial discontinuities between similarlycolored and/or textured objects may only manifest when the pels of theobjects in the video frame are undergoing coherent motion relative tothe objects themselves, but different motion relative to each other. Thepresent invention utilizes a combination of spectral, texture, andmotion segmentation to robustly identify the spatial discontinuitiesassociated with a salient signal mode.

Temporal Segmentation

The temporal integration of translational motion vectors, orequivalently finite difference measurements in the spatial intensityfield, into a higher-order motion model is a form of motion segmentationthat is described in the prior art.

In one embodiment of the invention, a dense field of motion vectors isproduced representing the finite differences of object motion in thevideo. These derivatives are grouped together spatially through aregular partitioning of tiles or by some initialization procedure suchas spatial segmentation. The “derivatives” of each group are integratedinto a higher order motion model using a linear least squares estimator.The resulting motion models are then clustered as vectors in the motionmodel space using the k-means clustering technique. The derivatives areclassified based on which cluster best fits them. The cluster labels arethen spatially clustered as an evolution of the spatial partitioning.The process is continued until the spatial partitioning is stable.

In a further embodiment of the invention, motion vectors for a givenaperture are interpolated to a set of pet positions corresponding to theaperture. When the block defined by this interpolation spans pelscorresponding to an object boundary, the resulting classification issome anomalous diagonal partitioning of the block.

In the prior art, the least squares estimator used to integrate thederivatives is highly sensitive to outliers. The sensitivity cangenerate motion models that heavily bias the motion model clusteringmethod to the point that the iterations diverge widely.

In the present invention the motion segmentation methods identifyspatial discontinuities through analysis of apparent pel motion over twoor more frames of video. The apparent motion is analyzed for consistencyover the frames of video and integrated into parametric motion models.Spatial discontinuities associated with such consistent motion areidentified. Motion segmentation can also be referred to as temporalsegmentation, because temporal changes may be caused by motion. However,temporal changes may also be caused by some other phenomena such aslocal deformation, illumination changes, etc.

Through the described method, the salient signal mode that correspondsto the normalization method can be identified and separated from theambient signal mode (background or non-object) through one of severalbackground subtraction methods. Often, these methods statistically modelthe background as the pels that exhibit the least amount of change ateach time instance. Change can be characterized as a pel valuedifference. Alternatively, motion segmentation can be achieved given thedetected position and scale of the salient image mode. A distancetransform can be used to determine the distance of every pel from thedetected position. If the pel values associated with the maximumdistance are retained, a reasonable model of the background can beresolved. In other words, the ambient signal is re-sampled temporallyusing a signal difference metric.

Given a model of the ambient signal, the complete salient signal mode ateach time instance can be differenced. Each of these differences can bere-sampled into spatially normalized signal differences (absolutedifferences). These differences are then aligned relative to each otherand accumulated. Since these differences have been spatially normalizedrelative to the salient signal mode, peaks of difference will mostlycorrespond to pel positions that are associated with the salient signalmode.

Resolution of Non-Object

Given a resolved background image, the error between this image and thecurrent frame can be normalized spatially and accumulated temporally.Such a resolved background image is described in the “backgroundresolution” section.

The resulting accumulated error is then thresholded to provide aninitial contour. The contour is then propagated spatially to balanceerror residual against contour deformation.

Gradient Segmentation

The texture segmentation methods, or equivalently, intensity gradientsegmentation, analyze the local gradient of the pels in one or moreframes of video. The gradient response is a statistical measure whichcharacterizes the spatial discontinuities local to a pel position in thevideo frame. One of several spatial clustering techniques is then usedto combine the gradient responses into spatial regions. The boundariesof these regions are useful in identifying spatial discontinuities inone or more of the video frames.

In one embodiment of the invention, the summed area table concept fromcomputer graphics texture generation is employed for the purpose ofexpediting the calculation of the gradient of the intensity field. Afield of progressively summed values is generated facilitating thesummation of any rectangle of the original field through four lookupscombined with four addition operations.

A further embodiment employs the Harris response which is generated foran image and the neighborhood of each pel is classified as being eitherhomogeneous, an edge, or a corner. A response value is generated fromthis information and indicates the degree of edge-ness or cornered-nessfor each element in the frame.

Multi-Scale Gradient Analysis

An embodiment of the present invention further constrains the imagegradient support by generating the image gradient values through severalspatial scales. This method can help qualify the image gradient suchthat spatial discontinuities at different scales are used to supporteach other—as long as an “edge” is discriminated at several differentspatial scales, the edge should be “salient”. A more qualified imagegradient will tend to correspond to a more salient feature.

In a preferred embodiment, the texture response field is firstgenerated, the values of this field are then quantized into several binsbased on a k-means binning/partitioning. The original image gradientvalues are then progressively processed using each bin as an interval ofvalues to which a single iteration can apply watershed segmentation. Thebenefit of such an approach is that homogeneity is defined in a relativesense with a strong spatial bias.

Spectral Segmentation

The spectral segmentation methods analyze the statistical probabilitydistribution of the black and white, grayscale, or color pels in thevideo signal. A spectral classifier is constructed by performingclustering operations on the probability distribution of those pels. Theclassifier is then used to classify one or more pels as belonging to aprobability class. The resulting probability class and its pels are thengiven a class label. These class labels are then spatially associatedinto regions of pels with distinct boundaries. These boundaries identifyspatial discontinuities in one or more of the video frames.

The present invention may utilize spatial segmentation based on spectralclassification to segment pels in frames of the video. Further,correspondence between regions may be determined based on overlap ofspectral regions with regions in previous segmentations.

It is observed that when video frames are roughly made up of continuouscolor regions that are spatially connected into larger regions thatcorrespond to objects in the video frame, identification and tracking ofthe colored (or spectral) regions can facilitate the subsequentsegmentation of objects in a video sequence.

Background Segmentation

The described invention includes a method for video frame backgroundmodeling that is based on the temporal maximum of spatial distancemeasurements between a detected object and each individual pel in eachframe of video. Given the detected position of the object, the distancetransformation is applied, creating a scalar distance value for each pelin the frame. A map of the maximum distance over all of the video framesfor each pel is retained. When the maximum value is initially assigned,or subsequently updated with a new and different value, thecorresponding pel for that video frame is retained in a “resolvedbackground” frame.

Appearance Modeling

A common goal of video processing is often to model and preserve theappearance of a sequence of video frames. The present invention is aimedat allowing constrained appearance modeling techniques to be applied inrobust and widely applicable ways through the use of preprocessing. Theregistration, segmentation, and normalization described previously areexpressly for this purpose.

The present invention discloses a means of appearance variance modeling.The primary basis of the appearance variance modeling is, in the case ofa linear model, the analysis of feature vectors to reveal compact basisexploiting linear correlations. Feature vectors representing spatialintensity field pels can be assembled into an appearance variance model.

In an alternative embodiment, the appearance variance model iscalculated from a segmented subset of the pels. Further, the featurevector can be separated into spatially non-overlapping feature vectors.Such spatial decomposition may be achieved with a spatial tiling.Computational efficiency may be achieved through processing thesetemporal ensembles without sacrificing the dimensionality reduction ofthe more global PCA method.

When generating an appearance variance model, spatial intensity fieldnormalization can be employed to decrease PCA modeling of spatialtransformations.

PCA

The preferred means of generating an appearance variance model isthrough the assembly of frames of video as pattern vectors into atraining matrix, or ensemble, and application of Principal ComponentAnalysis (PCA) on the training matrix. When such an expansion istruncated, the resulting PCA transformation matrix is employed toanalyze and synthesize subsequent frames of video. Based on the level oftruncation, varying levels of quality of the original appearance of thepels can be achieved.

The specific means of construction and decomposition of the patternvectors is well known to one skilled in the art.

Given the spatial segmentation of the salient signal mode from theambient signal and the spatial normalization of this mode, the pelsthemselves, or equivalently, the appearance of the resulting normalizedsignal, can be factored into linearly correlated components with a lowrank parameterization allowing for a direct trade-off betweenapproximation error and bit-rate for the representation of the pelappearance.

As shown in FIG. 2, the normalized object pels (242 & 244) can beprojected into a vector space and the linear correspondences can bemodeled using a decomposition process (250) such as PCA in order toyield a dimensionally concise version of the data (252 & 254).

Sequential PCA

PCA encodes patterns into PCA coefficients using a PCA transform. Thebetter the patterns are represented by the PCA transform, the fewercoefficients are needed to encode the pattern. Recognizing that patternvectors may degrade as time passes between acquisition of the trainingpatterns and the patterns to be encoded, updating the transform can helpto counter act the degradation. As an alternative to generating a newtransform, sequential updating of existing patterns is morecomputationally efficient in certain cases.

Many state-of-the-art video compression algorithms predict a frame ofvideo from one or more other frames. The prediction model is commonlybased on a partitioning of each predicted frame into non-overlappingtiles which are matched to a corresponding patch in another frame and anassociated translational displacement parameterized by an offset motionvector. This spatial displacement, optionally coupled with a frameindex, provides the “motion predicted” version of the tile. If the errorof the prediction is below a certain threshold, the tile's pels aresuitable for residual encoding; and there is a corresponding gain incompression efficiency. Otherwise, the tile's pels are encoded directly.This type of tile-based, alternatively termed block-based, motionprediction method models the video by translating tiles containing pels.When the imaged phenomena in the video adheres to this type of modeling,the corresponding encoding efficiency increases. This modelingconstraint assumes a certain level of temporal resolution, or number offrames per second, is present for imaged objects undergoing motion inorder to conform to the translational assumption inherent in block-basedprediction. Another requirement for this translational model is that thespatial displacement for a certain temporal resolution must be limited;that is, the time difference between the frames from which theprediction is derived and the frame being predicted must be a relativelyshort amount of absolute time. These temporal resolution and motionlimitations facilitate the identification and modeling of certainredundant video signal components that are present in the video stream.

Residual-Based Decomposition

In MPEG video compression, the current frame is constructed by motioncompensating the previous frame using motion vectors, followed byapplication of a residual update for the compensation blocks, andfinally, any blocks that do not have a sufficient match are encoded asnew blocks.

The pels corresponding to residual blocks are mapped to pels in theprevious frame through the motion vector. The result is a temporal pathof pels through the video that can be synthesized through the successiveapplication of residual values. These pels are identified as ones thatcan be best represented using PCA.

Occlusion-Based Decomposition

A further enhancement of the invention determines if motion vectorsapplied to blocks will cause any pels from the previous frame to beoccluded (covered) by moving pels. For each occlusion event, split theoccluding pels into a new layer. There will also be revealed pelswithout a history. The revealed pels are placed onto any layer that willfit them in the current frame and for which a historical fit can be madefor that layer.

The temporal continuity of pels is supported through the splicing andgrafting of pels to different layers. Once a stable layer model isarrived at, the pels in each layer can be grouped based on membership tocoherent motion models.

Sub-Band Temporal Quantization

An alternative embodiment of the present invention uses discrete cosinetransform (DCT) or discrete wavelet transform (DWT) to decompose eachframe into sub-band images. Principal component analysis (PCA) is thenapplied to each of these “sub-band” videos. The concept is that sub-banddecomposition of a frame of video decreases the spatial variance in anyone of the sub-bands as compared with the original video frame.

For video of a moving object (person), the spatial variance tends todominate the variance modeled by PCA. Sub-band decomposition reduces thespatial variance in any one decomposition video.

For DCT, the decomposition coefficients for any one sub-band arearranged spatially into a sub-band video. For instance, the DCcoefficients are taken from each block and arranged into a sub-bandvideo that looks like a postage stamp version of the original video.This is repeated for all the other sub-bands, and the resulting sub-bandvideos are each processed using PCA.

For DWT, the sub-bands are already arranged in the manner described forDCT.

In a non-limiting embodiment, the truncation of the PCA coefficients isvaried.

Wavelet

When a data is decomposed using the discrete Wavelet transform (DWT),multiple band-pass data sets result at lower spatial resolutions. Thetransformation process can be recursively applied to the derived datauntil only single scalar values results. The scalar elements in thedecomposed structure are typically related in a hierarchicalparent/child fashion. The resulting data contains a multi resolutionhierarchical structure and also finite differences as well.

When DWT is applied to spatial intensity fields, many of the naturallyoccurring images' phenomena are represented with little perceptual lossby the first or second low band pass derived data structures due to thelow spatial frequency. Truncating the hierarchical structure provides acompact representation when high frequency spatial data is either notpresent or considered noise.

While PCA may be used to achieve accurate reconstruction with a smallnumber of coefficients, the transform itself can be quite large. Toreduce the size of this “initial” transform, an embedded zero tree (EZT)construction of a wavelet decomposition can be used to build aprogressively more accurate version of the transformation matrix.

Subspace Classification

The present invention represents several discrete parameterizations asan element of vectors. These parameters include, in a non-limiting way,the pels in the normalized appearance of the segmented object, themotion parameters, and any structural positions of features or verticesin two or three dimensions. Each of these vectors exists in a vectorspace, and the analysis of the geometry of the space is used to yieldconcise representations of the parameter vectors. Beneficial geometriesinclude subspaces that are centered on clusters of vectors. When severalsubspaces are mixed, creating a more complex subspace, the individualsimpler subspaces can be difficult to discern. There are several methodsof segmentation that allow for the separation of such subspaces throughexamining the data in a higher dimensional vector space that is createdthrough some interaction of the original vectors (such as innerproduct).

One method of segmenting the vector space involves the projection of thevectors into a Veronese vector space representing polynomials. Thismethod is well known in the prior art as the Generalized PCA or GPCAtechnique. Through such a projection, the normals to the polynomials arefound, and the vectors associated with those normals can be groupedtogether. An example of the utility of this technique is in thefactoring of two dimensional spatial point correspondences tracked overtime into a three dimensional structure model and the motion of thatthree dimensional model.

Hybrid Spatial Normalization Compression

The present invention extends the efficiency of block-based motionpredicted coding schemes through the addition of segmenting the videostream into two or more “normalized” streams. These streams are thenencoded separately to allow the conventional codec's translationalmotion assumptions to be valid. Upon decoding the normalized streams,the streams are de-normalized into their proper position and compositedtogether to yield the original video sequence.

In one embodiment, one or more objects are detected in the video streamand the pels associated with each individual object are subsequentlysegmented leaving non-object pels. Next, a global spatial motion modelis generated for the object and non-object pels. The global model isused to spatially normalize object and non-object pels. Such anormalization has effectively removed the non-translational motion fromthe video stream and has provided a set of videos whose occlusioninteraction has been minimized. These are both beneficial features ofthe present inventive method.

The new videos of object and the non-object, having their pels spatiallynormalized, are provided as input to a conventional block-basedcompression algorithm. Upon decoding of the videos, the global motionmodel parameters are used to de-normalize those decoded frames, and theobject pels are composited together and onto the non-object pels toyield an approximation of the original video stream.

As shown in FIG. 6, the previously detected object instances (206 & 208)for one or more objects (630 & 650), are each processed with a separateinstance of a conventional video compression method (632). Additionally,the non-object (602) resulting from the segmentation (230) of theobjects, is also compressed using conventional video compression (632).The result of each of these separate compression encodings (632) areseparate conventional encoded streams for each (634) corresponding toeach video stream separately. At some point, possibly aftertransmission, these intermediate encoded streams (234) can bedecompressed (636) into a synthesis of the normalized non-object (610)and a multitude of objects (638 & 658). These synthesized pels can bede-normalized (640) into their de-normalized versions (622, 642 & 662)to correctly position the pels spatially relative to each other so thata compositing process (670) can combine the object and non-object pelsinto a synthesis of the full frame (672).

Integration of Hybrid Codec

In combining a conventional block-based compression algorithm and anormalization-segmentation scheme, as described in the presentinvention, there are several inventive methods that have resulted.Primarily, there are specialized data structures and communicationprotocols that are required.

The primary data structures include global spatial deformationparameters and object segmentation specification masks. The primarycommunication protocols are layers that include the transmission of theglobal spatial deformation parameters and object segmentationspecification masks.

1. A computer apparatus for the purpose of generating an encoded form ofvideo signal data from a plurality of video frames, comprising: a meansfor identifying corresponding elements of an object between two or morevideo frames; a means for modeling such correspondences to generatecorrespondence models, where the correspondence models include motionmodels and appearance models; a means for segmenting and resampling peldata in said video frames associated with said object, said segmentingand resampling means utilizing said correspondence models, where there-sampled pel data creates a representation of said object based onfinite differences between said video frames, the finite differencesimplying an interpolant on a neighborhood of pels in said video frames;and a means for restoring spatial positions of the re-sampled pel data,said restoring means utilizing the correspondence models; wherein saidobject is one or more objects, and said re-sampled pel data is anintermediate form of original pel data of the object for videoprocessing.
 2. The computer apparatus of claim 1 wherein the object istracked by a tracking member, comprising: a means of detecting theobject in a video frame sequence; and a means of tracking said objectthrough two or more frames of the video frame sequence; wherein saidobject detection means and said tracking means employ a Viola/Jones facedetection algorithm.
 3. The computer apparatus of claim 1 wherein: thesegmenting and resampling means includes a segmenter; and the object issegmented from a video frame using a spatial segmentation method,implemented by: the segmenter segmenting said pel data associated withsaid object from other pel data in said two or more video frames, thesegmenter generating associated segmentation data and; a means ofcomposing said restored pel data together with the associatedsegmentation data to create a representation of an original video frame;wherein said segmenter employes temporal integration.
 4. The computerapparatus of claim 1 wherein the correspondence models are factored intoglobal models, by: a means of integrating correspondence measurementsinto a model of global motion; said correspondence modeling meanscomprising a robust sampling consensus for the solution of a twodimensional affine motion model, and said correspondence modeling meanscomprising a sampling population based on finite differences generatedfrom block-based motion estimation between two or more video frames. 5.The computer apparatus of claim 1 wherein the resampled pel data beingan intermediate form is normalized object pel data and further encodedby: a means of decomposing said normalized object pel data into anencoded representation; a means of recomposing said normalized objectpel data from the encoded representation; wherein said decomposing meanscomprises Principal Component Analysis, and said recomposing meanscomprises Principal Component Analysis.
 6. The computer apparatus ofclaim 5 wherein: (i) the means for identifying employs tracking meansfor tracking multiple objects; (ii) the correspondence modeling meansintegrates motion parameters in a manner that facilitates a compactparameterization of motion parameters in the correspondence models; and(iii) the segmenting and resampling means includes (a) segmenting meansfor spatially segmenting the pel data associated with said object fromother pel data in the two or more video frames, the segmentation meansgenerating segmentation data associated with the object, and (b) meansfor composing said restored pel data together with the associatedsegmentation data to form an original video frame, wherein saidsegmentation means includes temporal integration.
 7. The computerapparatus of claim 5 wherein pel data of a non-object of the videoframes are modeled, said non-object of the video frames being a residualwhen said object is removed from the video frames.
 8. The computerapparatus of claim 6 wherein the segmented and resampled pel data arecombined with a video compression/decompression process, including: ameans of supplying the re-sampled pel data as video data to the videocompression/decompression process; a means of storing and transmittingcorrespondence models along with corresponding encoded pel data suchthat said compression/decompression process has increased compressionefficiency.
 9. A method for generating an encoded form of video signaldata from a plurality of video frames, comprising the computerimplemented steps of: identifying corresponding elements of an objectbetween two or more video frames; modeling such correspondences togenerate correspondence models, where the correspondence models includemotion models and appearance models; segmenting and resampling pel datain said video frames associated with said object, said segmenting andresampling utilizing said correspondence models, where the re-sampledpel data creates a representation of said object using finitedifferences between said video frames, the finite differences implyingan interpolant on a neighborhood of pels in said video frames; andrestoring spatial positions of the re-sampled pel data, said restoringutilizing the correspondence models; wherein said object is one or moreobjects, and said re-sampled pel data provides an intermediate form oforiginal pel data of the object for video processing.
 10. A method asclaimed in claim 9 further comprising the steps of: detecting the objectin a video frame sequence; and tracking said object through two or moreframes of the video frame sequence.
 11. A method as claimed in claim 9wherein the step of segmenting and resampling includes the steps of:segmenting said pel data associated with said object from other pel datain said two or more video frames, the segmenting generating associatedsegmentation data; and composing said restored pel data together withthe associated segmentation data resulting in a representation of anoriginal video frame; wherein said segmenting includes temporalintegration.
 12. A method as claimed in claim 9 wherein thecorrespondence models are factored into global models, furthercomprising the step of: integrating correspondence measurements into amodel of global motion; said correspondence modeling including (i) asampling consensus for the solution of a two dimensional affine motionmodel, and (ii) a sampling population based on finite differencesgenerated from block-based motion estimation between two or more videoframes.
 13. A method as claimed in claim 9 further comprising the stepsof: decomposing said resampled object pel data into an encodedrepresentation; recomposing said resampled object pel data from theencoded representation; wherein said steps of decomposing andrecomposing employing Principal Component Analysis.
 14. A method asclaimed in claim 13 further comprising the step of (i) tracking multipleobjects; (ii) integrating motion parameters in a manner that facilitatesa compact parameterization of motion parameters in the correspondencemodels; and (iii) spatially segmenting the pel data associated with saidobject from other pel data in the two or more video frames, thesegmenting generating segmentation data associated with the object; and(iv) composing said restored pel data together with the associatedsegmentation data to form a representation of an original video frame.15. A method as claimed in claim 13 further comprising the step ofmodeling residuals of the video frames, said residuals being results ofa video frame when said one or more objects are removed.
 16. A method asclaimed in claim 13 further comprising the steps of: supplying there-sampled pel data as video data to a video compression/decompressionprocess; and storing and transmitting correspondence models along withthe encoded representation of pel data such that thecompression/decompression process has increased compression efficiency.